Fault Detection and Diagnostics for Rooftop Air Conditioners

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26 class of high cost fault, accounting for 10% of total service costs. Further analysis showed that, although most failures in hermetic compressors are diagnosed as a failure in the motor, those failures usually result from mechanical problems such as overload or liquid refrigerant in the compressor. Based on their survey and analysis, Breuker and Braun concluded that five fault types should be considered for systems with fixed expansion devices: (1) refrigerant leakage; (2) condenser fouling; (3) evaporator filter fouling; (4) liquid line restriction; and (3) compressor valve leakage. To evaluate the FDD technique presented by Rossi and Braun, the above five faults were introduced within a 3-ton fixed orifice air conditioner in well-controlled environmental chambers under various fault levels and cooling load levels. Results showed that refrigerant leakage, condenser fouling, and liquid line restriction faults could be detected and diagnosed before an 8% reduction in COP occurred; compressor valve leakage was detected and diagnosed before a 12% reduction occurred; and the least sensitivity was evaporator fouling at 20%. These results are compared with the improved FDD technique later in the current report. To keep track of the up-to-date research, Comstock, Chen, and Braun (1999) performed an exhaustive literature review of FDD in HVAC. This review provided a solid background and guide for later research. The fault characteristics on a system with a TXV are different from those with a fixed orifice for which Rossi and Braun originally developed the statistical rule-based technique. Chen (2000a) modified and evaluated the original FDD technique for a 5-ton rooftop unit with a TXV as the expansion device. To simplify the FDD method, two innovative and easy-to-implement methods were proposed (Chen & Braun, 2000b & 2001). The first method, termed the “Sensitivity Ratio Method”, used measurements and model predictions of temperatures for normal system operation to compute ratios that are sensitive to individual faults. The second method, termed the “Simple Rule-Based Method”, dispensed with any on-line model but used performance indices computed from raw measurements that are relatively independent of operating state but are sensitive to faults. Both methods were tested using experimental data for different fault types and
27 fault levels at different operation conditions. Also, a 7.5-ton unit from the same product family as the 5-ton unit was used for a robustness test. Ghiaus (1999) presented a bond graph method for a packaged air conditioning system. The bond graph is a graph in which nodes represent conservation of energy equations, and terminal nodes represent either system elements (such as resistance, capacitance, inertia) or sources. A bond is a power connection between two parts of the system: A and B. The power is the product of power variables: effort and flow. Effort represents force, torque, pressure, voltage, or absolute temperature, while flow represents velocity, rotational frequency, volume flow rate, current, or entropy flow rate. For the air conditioning system, a thermal bond graph used temperature as effort and entropy flow rate as flow. Two faults: reducing the heat removed by the evaporator (by slowing the evaporator fan) and reducing the heat rejected by the condenser (by slowing the condenser fan) were considered. The advantage of the method is that it could diagnose a fault without any a priori knowledge of the possible faults and implementation of the fault inference algorithm is fast and simple due to its recursive nature. However, there are several drawbacks for this method. Firstly, the method does not consider impact of variation in driving conditions on the bond graph, so it cannot tell driving condition effects from faulty effects. Secondly, only two simple faults were considered. If there is a refrigerant fault such as leakage or flow restriction, the technique described in the paper could not make the correct diagnosis, due to the assumption of constant refrigerant flow rate. 1.2.2.2 Other HVAC&R Systems There is a large body of literature on other HVAC systems, especially variable air volume (VAV) air handling units (AHU) and chillers. Since this project is focused on rooftop and other packaged air conditioners, only recent and representative research is discussed here. Shaw and Norford (2002) presented two techniques for using electrical power data for detecting and diagnosing a number of faults in AHUs. One technique relies on gray-box correlations of electrical power with such exogenous variables as airflow or
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CALIFORNIA ENERGY COMMISSION Final
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Acknowledgements Jim Braun and Haor
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Abstract Project 2.1, Fault Detecti
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TABLE OF CONTENTS LIST OF TABLES LI
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LIST OF FIGURES Page 1 - Field Test
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The report “Description of Labora
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mounted heat pumps for heating and
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The retail stores are in Southern C
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Figure 1 - Field Test Sites Data Co
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Gibson School (Cont’d) Woodland S
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Table 1 - Data List for Modular Sch
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BUILDING TYPE: Modular School Rooms
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HEATING / AIR CONDITIONING EQUIPMEN
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Sacramento Area McDonalds PlayPlace
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Bradshaw Road (Sacramento Area) McD
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TEST INSTRUMENTATION: Tables 2 and
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Table 2 - Data List for Inland Rest
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Castro Valley (San Francisco Bay Ar
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Castro Valley McDonalds PlayPlace P
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more or less custom design, publish
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BUILDING TYPE: Retail Store ADDRESS
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TEST INSTRUMENTATION: Similar test
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• Temperature and humidity levels
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November 2001 - December/January 20
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Table of Contents 1. Introduction..
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1. Introduction All the thermodynam
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Table 1.1 Comparisons of Three Mode
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solution procedure involves the non
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independent of the moisture content
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function, f ( X , y) , if it can be
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Figure 2.1 Neural-Network Implement
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prototype was developed by using th
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3. Comparison of black-box modeling
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which is more accurate than the exp
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Similar to GRNN, RBF has very good
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Table 3.2 RMS error (Polynomial,GRN
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Table 3.4 Contrast of Black-box mod
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Polynomial plus GRNN Training Desir
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interpolation(poly+GRNN) extrapolat
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used to build the steady-state mode
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α , ρ, τ I t t s h o A t a Figur
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Temperature (F) 82 79 76 73 Condens
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0.050 0.045 Pressure = 101.3 [kPa]
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T T − T − T W = W −W −W m =
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Figure 4.8 Output of three steady-s
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will be calculated to represent the
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180 175 RMS error = 0.3984 (F) Pred
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180 175 RMS error =1.1472 (F) Predi
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51 50 RMS error=0.3860 (F) Predicte
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4.4 California field site results S
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105 100 RMS error =0.5982 (F) Predi
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Since air conditioners always cycle
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6. Conclusions and future work So f
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Lee, W., House, J.M. and Shin, D.R.
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Table of Contents 1 Introduction...
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AHU α β 2 d i EER ∆ ∆ η v T
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$ !! !! )
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Paper statistics in HVAC FDD Number
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011>" - , ?" 8?"
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+ : 6336" ,
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+ :: !!- :: !!
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6 24 122 7) 6 3++, ) . !! /011
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)
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336 F / s $ S
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N $ V v1
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Figure 3-4 2-dimensional residual d
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10 5 Normal and current operation p
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7 N Ω f N N Ω = Ω X ,
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0 + α 6 d χ ( )
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Normal operation region Residual-2
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Table 3-2 Refrigerant Leak at 20% l
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ratio dist = P F 1 P F 1 2 1 9.0
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$ , " - (
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)(( 0( 04 ) 6330" /
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Table 4-2 Polynomial plus GRNN mode
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4.2 m r,predict (kg/min) 4 3.8 3.6
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6 1)( ( ( 6 1)( ) , ( )
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- )
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? "J> *"J060 #"J083 *$"J670 #"J08<
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Table 4-17 Detected (normal, fault)
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c=1 c=10 c=20 1 0.8 Distance ratio
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$ /) $ 0111" .. !!
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Chen, Bin and Braun, J.E, 2000. Sim
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Ventilating, Air-Conditioning and R
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1.T amb 2.T ra 3.RH ra Plant Prepro
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F) $
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+: + $ 5
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.44(' * 5 09( ( 0( F N ( M , Σ)
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Load 20% 40% 60% 80% 100% 3 4 Fault
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Load 20% 40% 60% 80% 100% 3 4 Fault
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3 4 0 0 0 0 0.4173 0 0.0010 0 0.000
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.44(' $ () ) ) - $
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∆P Restrictio n Level=100%* ∆P
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2 TABLE OF CONTENTS TABLE OF CONTEN
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4 A1.2.3 Valve Position Expression
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6 Figure 2-10 Decoupling refrigeran
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8 LIST OF TABLES Table 1-1 Fault di
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10 N = Number of generated sample p
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12 with a fixed-orifice as the expa
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14 (residuals) should have a zero m
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16 expected distribution of the res
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18 operation. Another advantage is
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20 1.1.2.1 Original SRB Fault Diagn
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22 Corresponding to the SRB fault d
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24 integration of the probability d
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26 1. Simplifies fault detection fr
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28 Step i Do FDD on fault i . After
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30 ROOFTOP UNIT FAULTS COMPONENT-LE
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32 has two possible causes: refrige
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34 1.2.3 Decoupling of Component Fa
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36 1.2.3.2 Condenser-Related Faults
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38 mixture, χ ref is known as the
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40 Table 1-3 also lists the refrige
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42 Refrigerant Property T cond, pre
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44 as an independent feature only f
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46 evaporator air flow rate reducti
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48 5. 2 Pll ∆ deviates drasticall
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50 System-Level Faults RefUnder Ref
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52 1.2.5 Summary of Decoupling Sche
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54 noise, system disturbances and m
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56 compressor data. When there is a
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58 Figure 2-5 Decoupling evaporator
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60 Total Pressure Drop of Liquid- L
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62 Figure 2-11 Illustration of Demo
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64 bypass the compressor. At this t
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66 for T dis because there is no ov
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68 Figure 2-14 Outputs of the FDD d
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70 the system requires more refrige
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72 recommended that: the system req
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74 Figure 2-20 Outputs of the FDD d
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76 Figure 2-21 Histogram bar plot o
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82 2.3.2 Summarized Results for Oth
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84 3 CONCLUSIONS AND RECOMMENDATION
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86 Breuker, M.S. and Braun, J. E.,
Page 279 and 280: 88 Lee, W., House, J.M. and Shin, D
Page 281 and 282: 90 APPENDIX 1 PHYSICAL MODELS OF EX
Page 283 and 284: 92 A1.1.2 Short-Tube Models Many re
Page 285 and 286: 94 m& = CA ρ P up − P ) (A1-5) (
Page 287 and 288: 96 The abrupt change of CA for the
Page 289 and 290: 98 These equations can be combined
Page 291 and 292: 100 H h T sh , max opening T sh , s
Page 293 and 294: 102 A1.2.5 Parameter Estimation Met
Page 295 and 296: 104 T (2 T sh, ratingopening , sh,m
Page 297 and 298: 106 Harms’Result Harms plotted al
Page 299 and 300: 108 Mass flow rate Local linearizat
Page 301 and 302: 110 temperature on the RTD is unifo
Page 303 and 304: 112 in cold water application, it i
Page 305 and 306: 1 ACKNOWLEDGEMENTS The research tha
Page 307 and 308: 3 2.2.3 Todd Harms’ Data ........
Page 309 and 310: 5 LIST OF FIGURES Figure E-1. FDD d
Page 311 and 312: 7 LIST OF TABLES Table E-1 FDD resu
Page 313 and 314: 9 IA = Independence Assumption λ i
Page 315 and 316: 11 EXECUTIVE SUMMARY Packaged air c
Page 317 and 318: 13 current values of the fault indi
Page 319 and 320: 15 Figure E-4 Histogram of the EER
Page 321 and 322: 17 2. Operational cost savings, whi
Page 323 and 324: 19 Table E-5 Conservative Lifetime
Page 325 and 326: 21 constraints. Economic constraint
Page 327 and 328: 23 Paper statistics in HVAC FDD 35
Page 329: 25 on directional changes to identi
Page 333 and 334: 29 was concluded that the method wa
Page 335 and 336: 31 However, several improvements ar
Page 337 and 338: 33 point sensor placement is genera
Page 339 and 340: 35 2 DATA SOURCES USED FOR EVALUATI
Page 341 and 342: 37 The fives types of faults are re
Page 343 and 344: 39 Occupation Type Climate Location
Page 345 and 346: 41 The SRB FDD method determines wh
Page 347 and 348: 43 The following sections describe
Page 349 and 350: 45 points rather than on a distribu
Page 351 and 352: 47 current operation point P 1, P F
Page 353 and 354: 49 4 A DECOUPLING-BASED FDD TECHNIQ
Page 355 and 356: 51 This approach overcomes the draw
Page 357 and 358: 53 ⎡ ∆T ⎢ ⎢ ∆T 2 ⎢ ∆
Page 359 and 360: 55 improving the compressor model p
Page 361 and 362: 57 Condenser Air Mass Flow Rate (lb
Page 363 and 364: 59 Evaporator Air Mass Flow Rate (l
Page 365 and 366: 61 Liquid-Line Pressure Drop (PSI)
Page 367 and 368: 63 4.2.2 Purdue Field Emulation Sit
Page 369 and 370: 65 1. Evacuated the system, and the
Page 371 and 372: 67 normal _ value − current _ val
Page 373 and 374: 69 Figure 4-16 shows one frame afte
Page 375 and 376: 71 Figure 4-18 shows one frame afte
Page 377 and 378: 73 valve position can be seen from
Page 379 and 380: Figure 4-22 Outputs of the FDD demo
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83 4.2.3.2 Summarized Results for O
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85 5 ECONOMIC ASSESSMENTS Since the
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87 inspection savings would be $2,0
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89 Q & Cap = W & × EER (5-2) The e
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91 4. electricity costs ( C e ). Ut
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93 Table 5-3 lists estimates of equ
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95 5.4 Smart Service Schedule Savin
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97 7. A 6-ton RTU having a cost of
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99 Location North California South
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101 5. Three case studies were inve
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103 REFERENCES Aaron, D. A., and P.
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105 Davis, Coby. 1993. Comparison o
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107 Rossi, T.M., 1995. Detection, D
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